Abstract
One of the future directions of content-based image retrieval (CBIR) systems is incremental learning of indexing and retrieval algorithms. Optimization of the indexing algorithm is more difficult compared to the retrieval algorithm enhancement; since each time the indexing algorithm parameters are modified, all images of the reference database should be indexed again. This paper considers, for the first time, a challengeable limitation of actual indexing optimization systems: learning in dynamic and incremental CBIR environments. We introduce a new incremental evolutionary optimization method based on evolutionary group algorithm. The new incremental evolutionary group algorithm (IEGA) overcomes time-consuming drawbacks related to general evolutionary algorithms in large scale content-based image indexing optimization tasks and presents a new strategy that is enhanced with the ability of incremental learning. Evaluation results on some simulated dynamic CBIR systems show that the proposed method can continuously obtain good performance in the presence of environmental or scale changes.
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Notes
In practice there is at least one image in the imagebase (i.e. \(\vert D_{0}\vert =1\)).
References
Thomee B, Lew MS (2012) Interactive search in image retrieval: a survey. Int J Multimed Info Retr 1:71–86
Wang JZ, Geman D, Jiebo L, Gray R (2008) Special issue on real-world image annotation and retrieval. IEEE Trans Pattern Anal Mach Intell 30(11):1873–1876
Datta R, Joshi D, Li J, Wang J (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60
Lew M, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1): 1–19
Gevers T, Weijer J, Stokman H (2007) Color feature detection: an overview. In: Lukac R, Plataniotis K (ed) Color image processing: methods and applications. CRC Press, Boca Raton, pp 203–226
Huang J, Kumar S, Mitra M, Zhu W, Zabih R (1997) Image indexing using color correlograms. In: Proceeding of the IEEE computer society conference on computer vision and pattern recognition (CVPR1997), San Juan, Puerto Rico, pp 762–768
Howarth P, Ruger S (2004) Evaluation of texture features for content-based image retrieval. In: Lecture Notes in Computer Science. Springer, Berlin, pp 326–334
Mahmoudi F, Shanbehzadeh J, Eftekhari-Moghadam A, Soltanian-Zadeh H (2003) Image retrieval based on shape similarity by edge orientation autocorrelogram. Pattern Recognit 36(8): 1725–1736
Saadatmand-Tarzjan M, Moghaddam HA (2007) A novel evolutionary approach for optimizing content-based image indexing algorithms. IEEE Trans Syst Man Cybern Part B Cybern 37(1):139–153
Moghaddam HA, Khajoie T, Rouhi AH, Saadatmand-Tarzjan M (2005) Wavelet correlogram: a new approach for image indexing and retrieval. Pattern Recognit 38(12):2506–2518
Pavlidis T (2008) Limitations of content-based image retrieval. In: Invited plenary talk at the 19th international conference on pattern recognition, Tampa, Florida. http://www.theopavlidis.com/technology/CBIR/PaperB/icpr08.htm. Accessed 26 Dec 2010
Wu S, Rahman MK, Chow TW (2005) Content-based image retrieval using growing hierarchical self-organizing quad tree map. Pattern Recognit 38(5):707–722
Khalid Sh (2012) Incremental indexing and retrieval mechanism for scalable and robust shape matching. Multimed Syst 18:319–336
Li B, Yuan S (2005) Incremental hybrid Bayesian network in content-based image retrieval. In: Proceedings of the Canadian conference on electrical and computer engineering. Saskatoon, Sask, pp 2025–2028
He X (2005) Image retrieval based on incremental subspace learning. Pattern Recognit 38(11):2047–2054
Shah B, Raghavan V, Dhatric P (2004) Efficient and effective content-based image retrieval using space transformation. In: Proceedings of the 10th international multimedia modeling conference (MMM’04) , Brisbane, Australia, pp 279
Huiskes MJ, Thomee B, Lew MS (2010) New trends and ideas in visual concept detection. In: Proceedings of the ACM international conference on multimedia information retrieval (MIR ’10), pp 527–536
Li Z, Lew MS (2012) Cost-sensitive learning in social image tagging: review, new ideas and evaluation. Int J Multimed Info Retr 1:205–222
Zhong D, Defée I (2005) DCT histogram optimization for image database retrieval. Pattern Recognit Lett 26:2272–2281
Rudolph G (1994) Convergence analysis of canonical genetic algorithms. IEEE Trans Neural Netw 5(1):96–101
Schettini R, Ciocca G, Zuffi S (2001) A survey on methods for color image indexing and retrieval in image databases. In: Luo R, MacDonald L (ed) Color imaging science: exploiting digital media. Wiley, Chichester
Wang JZ, Li J, Wiederhold G (2001) SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963
Müller H, Marchand-Maillet S, Pun T (2002) The truth about Corel—evaluation in image retrieval. In: Proceedings of international conference on image and video retrieval, pp 38–49
Goldberg DE (1999) Genetic and evolutionary algorithms in the real world. Illinois Genetic Algorithms Lab, Urbana, IL, Tech Rep 99013
Engelbrecht AP (2002) Computational intelligence. Wiley, UK
Whitley D (1989) The GENITOR algorithm and selective pressure. In: Proceedings of the 3rd International Conference on genetic algorithms, San Mateo, CA, pp 116–121
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Nikzad, M., Moghaddam, H.A. An incremental evolutionary learning method for optimizing content-based image indexing algorithms. Int J Multimed Info Retr 3, 41–52 (2014). https://doi.org/10.1007/s13735-013-0044-6
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DOI: https://doi.org/10.1007/s13735-013-0044-6